In this part of the website we explore how to deal with missing data. We begin by describing the various types of missing data and then describe some traditional approaches for dealing with missing data, including the shortcomings of these approaches.
We then describe some more advanced approaches, namely Multiple Imputation (MI) and Full Information Maximum Likelihood (FIML), and show how to use them in performing multiple regression.
- Types of Missing Data
- Traditional Approaches for Dealing with Missing Data
- Multiple Imputation (MI)
- Full Information Maximum Likelihood (FIML)